Trend detection from large text data
Hidenao Abe, Shusaku Tsumoto · 2010
In temporal text mining, some importance indices such as simple appearance frequency, tf-idf, and differences of some indices play the key role to point out remarkable trends of terms in sets of documents. However, almost of conventional methods have treated their remarkable trends as discrete statuses for each time-point or fixed period. In this paper, we present a method to find out remarkable temporal behaviors of technical terms by using several importance indices and temporal clustering on the indices. The implemented method with three indices and k-means clustering performed on research document sets. The results of the case study show that the method has a feasibility to point out emergent, popular, and subsiding terms based on the linear trend of the temporal clusters of the technical terms.